My colleagues and I would like to study the neuronal basis of complex behaviors in the nervous system of the medicinal leech. We will first complete the characterization of the interneuronal circuits responsible for four leech behaviors: local bending, shortening, swimming and crawling. We will then show how these circuits overlap and interact in determining the animal's response to mechanosensory stimulation. We have found recently that one of these behaviors, local bending, is produced by interneurons arranged in a distributed circuit. By using the back-propagation of error algorithm in neural network models, we have produced simulations which have many of the characteristics of the real interneuronal circuits. We propose to continue this approach, to determine whether the other three behavioral circuits are also distributed; there are strong indications that at least one of them is. In particular, we will study the mechanisms by which the nervous systems chooses among several possible behaviors, how the same interneurons contribute to several different behaviors, and how these networks are modified by learning and by embryological development. We will perform these experiments by recording intracellularly from neurons while the behaviors are being performed, then characterize the connections among the interneurons responsible for the behaviors. We will then use the neural network simulations to ask whether the identified circuit is complete and to suggest the properties of any undiscovered neurons. We will also use these neural networks to test whether there are other, possibly better, networks that can perform the same behaviors. Distributed networks are difficult to conceptualize, because every neuron contributes a little to every behavior and because some of the connections made by every neuron are inappropriate for each behavior. The neural network modeling techniques have given us a way to think about distributed networks and to make testable predictions about them. Such networks have been proposed for perception and motor control in more complex animals, but our work is the strongest indication that distributed networks control behaviors in a simple invertebrate. We are confident that our work can help to explain how such distributed systems process information and coordinate complex behaviors. Also, because we can measure many of the relevant neuronal parameters at the level of identified neurons, we should be able to help to refine the models and make them more biologically realistic. I believe that there will be major insights in the next decade into the ways that brains function as complex systems, and I feel that our integrated physiological and computational approach can contribute significantly to this understanding.